The Complete Machine Learning Roadmap

[00:01] engineer there are nine essential skills you need to master let's go over them you need to master let's go over them one by creating models that learn from data to make predictions or decisions as a

[00:15] master various skills from programming and math to deploying models first up you need to get proficient with python machine learning it's pretty simple and you can get a decent grasp of it in

[00:28] about a month or two now some jobs might ask for other languages like Java r or C++ for performance reasons but as a beginner just stick to python don't overwhelm Yourself by trying to learn everything at once focus on mastering

[00:41] python first the next thing you need to learn is a version control system like git git is not a programming language it's a tool we use to track changes to our code and collaborate with others git has a ton of features but you don't need

[00:53] to learn all of them think of it like the 8020 rule 80% of the time you use 20% of GS features so one to two weeks of practice is enough to get up and running next you need to dive into data structures and algorithms I know a lot

[01:07] of self-taught Engineers skip this step but trust me it's super important first off understanding these Concepts will really boost your problem solving skills which is key for tackling complex challenges plus big tech companies like

[01:19] Google Amazon and Facebook love to ask about data structures and algorithms in job interviews also using the right data structures helps you manage large data algorithms running smoothly so spend about a month or two on this and you'll

[01:35] be in a great shape next you need to get comfortable with SQL SQL stands for structured query language it's a very simple language we use for working with databases as a machine learning engineer you should know how to use SQL to access

[01:48] and organize the data you need for your models SQL is pretty simple and you can get a decent grasp of it in about a month or two now by the way to help you on this journey I've created a free supplementary PDF that breaks down the

[02:00] specific Concepts you need to learn for each skill it's a great resource to review your progress find gaps in your knowledge and prepare for interviews you can find the link in the description box also I have a bunch of tutorials on this

[02:12] website if you're looking for a structured learning again links are in the description box the next thing you need is a solid foundation in crucial because machine learning algorithms are built on these principles

[02:27] focus on linear algebra calculus probability and statistics these will help you understand how machine learning algorithms work and how to optimize them spend about 2 to 3 months to master these topics after that you need to get

[02:40] good at preparing and visualizing data for your models this means cleaning up the data and organizing it in a way that makes it easy for your model to understand you'll need to learn how to use tools like pandas and numpy to

[02:53] manipulate and clean the data once your data is clean you need to visualize it to understand patterns and communicate results python libraries like matplot lip and Seaborn will help you create insightful visualizations to identify

[03:06] Trends and anomalies if you have a solid background in Python and SQL you can get a good grasp of data pre-processing and visualization in a month or two now fundamentals machine learning algorithms fall into two categories supervised and

[03:21] unsupervised in supervised learning the model learns from labeled data meaning each input comes with a known output in unsupervised Lear learning the model works with unlabeled data and tries to figure out patterns and relationships on

[03:35] these types of algorithms and how they work you'll also need to get familiar with tools like tensor flow pie torch and pyed learn these are the essential tools used to build and train machine learning models dedicate about 3 to four

[03:49] months to master the core machine learning Concepts and how to use these tools effectively once you have a good understanding of the basics it's time to dive into more advanced machine learning Concepts this includes techniques like

[04:01] Ensemble learning which combines multiple models to improve performance and deep learning which involves neural networks with many layers you'll also need to learn about natural language processing or NLP for working with Text

[04:14] data and computer vision for working with images these Advanced topics will help you tackle more complex problems and build more sophisticated models spend about 2 to 3 months on these advanced concepts to deepen your

[04:26] Knowledge and Skills finally you need to know how to put the models you build into action this means learning how to create simple web services that let other applications use your models you can do this by learning python

[04:38] Frameworks like flask or Django you should also learn about Docker which is a tool that makes it easy to package your model and all its dependencies so it runs smoothly on any machine think of it like packing everything your model

[04:50] needs into a box so it works anywhere you take it spend one to two months mastering these Basics so you can confidently deploy your models and make them available for real word use so if you dedicate 3 to 5 hours every day you

[05:04] can follow this road map and pick up all the skills you need to apply for entrylevel machine learning jobs in about 12 to 20 months if you have any comments below I'll do my best to answer

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